Autonomous Robotic Platform Asymmetric Cognitive Team Control
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Solution Overview
Problem
Conventional robotic platform control methods require constant human intervention, are limited by line-of-sight restrictions, and are cognitively demanding, making them unsuitable for high-intensity situations and requiring additional resources, which reduces the effectiveness and versatility of unmanned vehicles.
Innovation Solution
The implementation of an asymmetric cognitive team (ACT) system that integrates sensors with team members to enable robots to understand the situation and select appropriate behaviors autonomously, eliminating the need for cumbersome operator control units (OCUs) and allowing robots to operate as effective team members without constant human direction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional remote control or teleoperation methods are used, then the robotic platform can be controlled by a human operator, but full-time operator attention is required and line-of-sight restrictions apply
Solution Approach 1:
The robotic platform is equipped with onboard sensors and processing capabilities that enable it to autonomously detect obstacles, navigate terrain, and adjust its path without continuous human intervention. The system serves itself by making real-time navigation decisions based on sensor data from cameras, LIDAR, and other detectors.
Solution Approach 2:
The robotic platform pre-processes environmental data using onboard sensors and cognitive algorithms to anticipate obstacles and plan navigation paths before encountering them. This preliminary processing enables proactive decision-making rather than reactive responses to operator commands.
2Extent of automation
If semi-autonomous map-based control is used, then the robot can follow GPS waypoints autonomously, but the system is slow, requires training, and is difficult to re-plan when unforeseen events occur
Solution Approach 1:
The navigation system dynamically adapts its path planning based on real-time sensor input and changing environmental conditions. Rather than following fixed pre-programmed routes, the system continuously recalculates optimal paths using cognitive algorithms that process sensor data and adjust navigation strategies on the fly.
Solution Approach 2:
The robotic platform incorporates continuous feedback loops where sensor data from onboard detectors is processed by cognitive algorithms that compare actual position and environmental conditions against planned paths, enabling real-time course correction and adaptive re-planning when obstacles or changes are detected.
3Ease of operation
If conventional operator control units (OCUs) are used, then the operator can control the robot, but the OCU is heavy and cumbersome
Solution Approach 1:
The control processing functions are extracted from the handheld OCU and relocated to cloud-based or remotely hosted computing systems. The onboard robot retains only essential sensors and communication hardware, while complex navigation and decision-making algorithms execute remotely, eliminating the need for heavy local processing equipment.
Solution Approach 2:
The system uses universal communication protocols and standardized interfaces that allow the robotic platform to be controlled through multiple devices including smartphones, tablets, or web browsers, replacing specialized heavy OCUs with lightweight multi-purpose computing devices that serve multiple functions.
4Productivity
If conventional control systems are used, then the robot can be operated, but additional personnel and resources are required, reducing effectiveness
Solution Approach 1:
The robotic platform performs self-navigation, self-obstacle-detection, and self-path-planning using onboard sensors and cognitive algorithms, eliminating the need for dedicated operators or support personnel for routine navigation tasks, thereby reducing personnel requirements while maintaining operational capability.
Data Source
AI summary
A system and method can provide a command and control paradigm for integrating robotic assets into human teams. By integrating sensor to detect human interaction, movement, physiology, and location, a net-centric system can permit command of a robotic platform without an OCU. By eliminating the OCU and maintaining the advantages of a robotic platform, a robot can be used in the place of a human without fatigue, being immune to physiological effects, capable of non-humanoid tactics, a longer potential of hours per day on-station, capable of rapid and structured information transfer, has a personality-free response, can operate in contaminated areas, and is line-replaceable with identical responses. A system for controlling a robotic platform can comprise at least one perceiver for collecting information from a human or the environment; a reasoner for processing the information from the at least one perceiver and providing a directive; and at least one behavior for executing the directive of the reasoner.